Electricity load forecasting is an important aspect of power system management. Improving forecasting accuracy ensures reliable electricity supply, grid operations, and cost savings. Often, collected data consist of Missing Values (MVs), anomalies, outliers, or other inconsistencies caused by power failures, metering errors, data collection errors, hardware failures, network failures, or other unexpected events. This study uses real-world data to investigate the possibility of using synthetically generated data as an alternative to filling in MVs. Three datasets were created from an original one based on different imputation methods. The imputation methods employed were linear interpolation, imputation using synthetic data, and a proposed hybrid method based on linear interpolation and synthetic data. The performance of the three datasets was compared using deep learning, machine learning, and statistical models and verified based on forecasting accuracy improvements. The findings demonstrate that the hybrid dataset outperformed the other interpolation methods based on the forecasting accuracy of the models.

Short-term electricity demand forecasting is a critical task for real-time decision-making. This research investigates the impact of solar irradiance components on electricity demand forecasting. The study utilized two years of hourly electricity data in addition to three key solar irradiance components: Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), and Diffuse Horizontal Irradiance (DHI), as clear sky indices and exogenous features. Extensive feature selection was performed to discover the best feature that enhances the forecasting accuracy of the model. Temporal Fusion Transformer (TFT) was employed as the primary model to study the influence of these exogenous variables on the electricity demand forecasting. The results demonstrated that use of solar irradiance data in the electricity demand forecasting improves the forecasting accuracy of the model. Particularly the combination of DHI and DNI clear sky indices produced the most accurate predictions. However, the addition of GHI introduced redundancy and reduced performance. The performance of the TFT model was also compared with various benchmark models. TFT outperformed the other compared models in various forecast horizons.